[ROCm][CI] Support async weight transfer example with platform-aware determinism (#35710)

Signed-off-by: Andreas Karatzas <akaratza@amd.com>

.buildkite/test-amd.yaml

@@ -1339,6 +1339,7 @@ steps:
   - tests/v1/entrypoints/openai/test_multi_api_servers.py
   - tests/v1/shutdown
   - tests/v1/worker/test_worker_memory_snapshot.py
+  - examples/offline_inference/new_weight_syncing/
   commands:
   # Work around HIP bug tracked here: https://github.com/ROCm/hip/issues/3876
   # TODO: Remove when the bug is fixed in a future ROCm release
@@ -1970,8 +1971,10 @@ steps:
 
 - label: Distributed Tests (4 GPUs) # 35min
   timeout_in_minutes: 50
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
   agent_pool: mi355_4
+  optional: true
+  # grade: Blocking
   working_dir: "/vllm-workspace/tests"
   num_gpus: 4
   source_file_dependencies:
@@ -2025,7 +2028,8 @@ steps:
   - popd
   # NEW rlhf examples
   - pushd ../examples/offline_inference/new_weight_syncing
-  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 rlhf.py
+  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 rlhf_nccl.py
+  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 rlhf_ipc.py
   - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 rlhf_async_new_apis.py
   - popd
 
@@ -2989,8 +2993,10 @@ steps:
 
 - label: Distributed Tests (2 GPUs) # 68min
   timeout_in_minutes: 90
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
   agent_pool: mi355_2
+  optional: true
+  # grade: Blocking
   working_dir: "/vllm-workspace/tests"
   num_gpus: 2
   source_file_dependencies:

examples/offline_inference/new_weight_syncing/rlhf_async_new_apis.py

@@ -47,12 +47,14 @@ from vllm.distributed.weight_transfer.nccl_engine import (
     NCCLWeightTransferInitInfo,
     NCCLWeightTransferUpdateInfo,
 )
+from vllm.platforms import current_platform
 from vllm.utils.network_utils import get_ip, get_open_port
 from vllm.v1.executor import Executor
 
 MODEL_NAME_V1 = "Qwen/Qwen3-1.7B-Base"
 MODEL_NAME_V2 = "Qwen/Qwen3-1.7B"
 PAUSE_TOKEN_THRESHOLD = 10
+ATTN_BACKEND = "TRITON_ATTN" if current_platform.is_rocm() else "FLASH_ATTN"
 
 
 class MyLLM(vllm.AsyncLLMEngine):
@@ -116,10 +118,16 @@ class TrainModel:
         from vllm.model_executor.layers.batch_invariant import (
             init_batch_invariance,
         )
+        from vllm.platforms import current_platform
         from vllm.v1.attention.backends.registry import AttentionBackendEnum
 
         # need to init all env vars for batch invariance which affect nccl ops
-        init_batch_invariance(AttentionBackendEnum.FLASH_ATTN)
+        attn_backend = (
+            AttentionBackendEnum.TRITON_ATTN
+            if current_platform.is_rocm()
+            else AttentionBackendEnum.FLASH_ATTN
+        )
+        init_batch_invariance(attn_backend)
 
         self.model = AutoModelForCausalLM.from_pretrained(
             model_name, dtype=torch.bfloat16
@@ -175,39 +183,56 @@ class TrainModel:
         return new_token_ids
 
 
-ray.init(
-    runtime_env={
-        "env_vars": {
-            # enable batch invariance for deterministic outputs
-            "VLLM_BATCH_INVARIANT": "1",
-            # prevent ray from setting CUDA_VISIBLE_DEVICES
+# Build platform-specific env vars for Ray
+ray_env_vars = {
+    # Prevent Ray from setting CUDA_VISIBLE_DEVICES
     "RAY_EXPERIMENTAL_NOSET_CUDA_ENV_VAR": "1",
-        }
-    }
-)
+}
+
+if current_platform.is_rocm():
+    # For ROCm, BATCH_INVARIANT vllm is not supported
+    ray_env_vars["VLLM_ROCM_USE_SKINNY_GEMM"] = "0"
+else:
+    # Enable batch invariance for deterministic outputs on NVIDIA
+    ray_env_vars["VLLM_BATCH_INVARIANT"] = "1"
+
+ray.init(runtime_env={"env_vars": ray_env_vars})
 
 # Launch the training model actor. Ray's resource scheduler will allocate
 # 1 GPU (via num_gpus=1 in the decorator), ensuring pg_inference gets different GPUs.
 train_model = TrainModel.remote(MODEL_NAME_V2)
 
-# Launch the vLLM inference engine. The `enforce_eager` flag reduces
-# start-up latency.
-# With data_parallel_backend="ray", vLLM's CoreEngineActorManager creates
-# its own placement groups internally for each DP rank, so we must NOT
-# create an outer placement group (it would reserve GPUs and hide them
-# from the internal DP resource check).
-llm = ray.remote(
-    num_cpus=0,
-    num_gpus=0,
-)(MyLLM).remote(
+rocm_determinism_kwargs = {}
+if current_platform.is_rocm():
+    # ROCm: To minimize non-determinism, we set fixed seed, no prefix caching, and
+    # sequential request processing (max_num_seqs=1).
+    rocm_determinism_kwargs = {
+        "seed": 0,
+        "enable_prefix_caching": False,
+        "max_num_seqs": 1,
+    }
+
+# Build platform-specific LLM kwargs
+llm_kwargs = dict(
     model=MODEL_NAME_V1,
     enforce_eager=True,
     max_model_len=8192,
     distributed_executor_backend="ray",
-    attention_backend="FLASH_ATTN",
+    attention_backend=ATTN_BACKEND,
     gpu_memory_utilization=0.75,
     weight_transfer_config=WeightTransferConfig(backend="nccl"),
 )
+llm_kwargs.update(rocm_determinism_kwargs)
+
+# Launch the vLLM inference engine.
+# With data_parallel_backend="ray", vLLM's CoreEngineActorManager creates
+# its own placement groups internally for each DP rank, so we must NOT
+# create an outer placement group (it would reserve GPUs and hide them
+# from the internal DP resource check).
+llm = ray.remote(
+    num_cpus=0,
+    num_gpus=0,
+)(MyLLM).remote(**llm_kwargs)
 
 PROMPTS = [
     "The president of the United States is",
@@ -304,25 +329,42 @@ for i, (output, pause_idx) in enumerate(results):
     print(f"    New weights ({n_after} tokens): {after_text!r}")
 
 # ── Phase 2: validate with a fresh V2 vLLM instance ────────────────
+# This validation relies on batch-invariant (deterministic) generation to
+# compare outputs from the weight-synced engine against a fresh V2 instance.
+# On NVIDIA, batch invariance is fully supported, so we require 100% exact
+# token match. On ROCm, batch invariance is not yet fully implemented
+# (see https://github.com/vllm-project/vllm/issues/27433 and
+# https://github.com/vllm-project/vllm/issues/33123), so residual
+# non-determinism (e.g. GEMM accumulation order, missing kernel overrides)
+# can cause single-token divergences that don't indicate a weight-sync
+# failure. We relax the pass rate to 90% on ROCm to accommodate this; a
+# real regression (broken weight transfer) would cause ~0% pass rate, not 90%+.
+MIN_PASS_RATE = 1.0 if not current_platform.is_rocm() else 0.9
+
 print(f"\n{'=' * 50}")
 print("VALIDATION: comparing weight-synced vLLM with fresh V2 instance")
+if current_platform.is_rocm():
+    print(f"  (ROCm mode: requiring >= {MIN_PASS_RATE:.0%} exact match rate)")
 print(f"{'=' * 50}")
 
 ray.get(llm.shutdown.remote())
 ray.kill(llm)
 ray.kill(train_model)
 
-llm_v2 = ray.remote(
-    num_cpus=0,
-    num_gpus=0,
-)(MyLLM).remote(
+llm_v2_kwargs = dict(
     model=MODEL_NAME_V2,
     enforce_eager=True,
     max_model_len=8192,
     gpu_memory_utilization=0.75,
     distributed_executor_backend="ray",
-    attention_backend="FLASH_ATTN",
+    attention_backend=ATTN_BACKEND,
 )
+llm_v2_kwargs.update(rocm_determinism_kwargs)
+
+llm_v2 = ray.remote(
+    num_cpus=0,
+    num_gpus=0,
+)(MyLLM).remote(**llm_v2_kwargs)
 
 val_futures = [
     llm_v2.do_generate.remote(
@@ -335,16 +377,17 @@ val_futures = [
 ]
 val_results = ray.get(val_futures)
 
-all_pass = True
+num_pass = 0
+num_total = len(results)
 for i, ((output, pause_idx), (val_output, _)) in enumerate(zip(results, val_results)):
     expected = list(output.outputs[0].token_ids)[pause_idx:]
     actual = list(val_output.outputs[0].token_ids)
     match = actual == expected
 
     if match:
+        num_pass += 1
         print(f"  [PASS] {PROMPTS[i]!r}")
     else:
-        all_pass = False
         print(f"  [FAIL] {PROMPTS[i]!r}")
         print(f"         weight-synced vLLM: {tokenizer.decode(expected)!r}")
         print(f"         V2 vLLM:           {tokenizer.decode(actual)!r}")
@@ -359,5 +402,14 @@ for i, ((output, pause_idx), (val_output, _)) in enumerate(zip(results, val_resu
 
 ray.get(llm_v2.shutdown.remote())
 ray.kill(llm_v2)
-assert all_pass, "Some prompts failed validation, see above for details"
+
+pass_rate = num_pass / num_total
+print(f"\n  Result: {num_pass}/{num_total} prompts passed ({pass_rate:.0%})")
+print(f"  Required: >= {MIN_PASS_RATE:.0%}")
+
+assert pass_rate >= MIN_PASS_RATE, (
+    f"Validation pass rate {pass_rate:.0%} ({num_pass}/{num_total}) "
+    f"is below the required {MIN_PASS_RATE:.0%} threshold. "
+    f"See failures above for details."
+)
 print("=" * 50)